Biology subjects

Findling, C.

Publications and source records attributed to Findling, C..

9 recordsLinked to original sources

bioRxiv2026

Augmenting the Bayesian Brain with learned and reusable world-model components for flexible cognition

The Bayesian Brain hypothesis assumes that cognition relies on internal generative models of the world, yet existing implementations remain constrained by pre-specified, task-specific generative structures and computationally heavy iterative inference schemes. Here, we introduce modular neural state-space models as a scalable realization of the Bayesian Brain, replacing fixed generative structures and pre-specified inference rules with learned world-model components and amortized neural updates. This framework preserves the core commitment to explaining observations through hidden causes while making inference learned and reusable rather than pre-specified and task-specific. Our modular implementation of these models affords learned components to be seamlessly recombined and stacked across superficially different tasks that share similar latent dynamics. Such computational reuse supports zero-shot generalization and predicts selective correlations of inference parameters between tasks. We confirm these key predictions in human behavior, identifying learned and reusable world-model components as a candidate computational principle for flexible cognition.

neuroscience↗

bioRxiv2025

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy RL cast doubt on a simple one-to-one mapping between neurotransmitters and computational mechanisms. Here, we detail a double-blind, placebo-controlled, randomised pharmacological study using the dopamine precursor L-DOPA, while healthy volunteers performed a volatile two-armed bandit task. Behaviourally, L-DOPA decreased switching behaviour following below-average rewards. Algorithmic RL modelling of human behaviour supported a dual effect of L-DOPA on the rate and precision of learning. By leveraging recurrent neural networks (RNNs) as implementational models of RL, we explain this dual effect through a single inference-time modulation, whereby L-DOPA triggers a positive reward bias at the input of the recurrent layer that implements RL. Our findings highlight a unifying mechanism at the implementation level that explain seemingly disparate algorithmic effects of dopamine.

neuroscience↗

bioRxiv2025

Altered Use of Prior Expectations and Modified Neural Dynamics in a Mouse Model of Autism

In dynamic environments, updating beliefs based on past experiences (priors) is essential for optimal decision-making. Prior utilization is often impaired in psychiatric disorders, affecting perception and behavior. We investigate how Neurexin1 (Nrxn1 ) loss-of-function disrupts this process, providing insight into circuit deficits underlying sensorimotor dysfunction. While the synaptic role of Nrxn1 role is well studied, its impact on network dynamics and decision-making behavior remain unclear. Using widefield calcium imaging, we assess cortex-wide activity in mice performing a two-choice task to probe how priors influence visually-guided decisions. This task requires the mouse to combine sensory evidence with the prior probability over the stimulus side. We find Nrxn1 KO mice underutilized priors and were slower to update choices based on feedback. During decision-making, cortex-wide cortical activity is both elevated and increasingly correlated in Nrxn1 KO mice, independent of task period. Moreover, a larger fraction of cortical variance was explained by movement variables, consistent with stronger coupling of cortical activity to motor signals and a bias toward movement-related dynamics. These findings suggest that core computations underlying decision-making, such as integrating past experience with current evidence, depend on intact synaptic mechanisms shaped by genes like Nrxn1.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Brain-wide representations of prior information in mouse decision-making

The neural representations of prior information about the state of the world are poorly understood. To investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACAd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

neuroscience↗

bioRxiv2020

Computation noise promotes cognitive resilience to adverse conditions during decision-making

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity and behavior, humans and other animals show a remarkable adaptability to unexpected adverse events occurring during task execution. This cognitive ability, described as constitutive of general intelligence, is missing from current artificial intelligence (AI) systems which feature exact (noise-free) computations. Here we show that implementing computation noise in recurrent neural networks boosts their cognitive resilience to a variety of adverse conditions entirely unseen during training, in a way that resembles human and animal cognition. In contrast to artificial agents with exact computations, noisy agents exhibit hallmarks of Bayesian inference acquired in a zero-shot fashion - without prior experience with conditions that require these computations for maximizing rewards. We further demonstrate that these cognitive benefits result from free-standing regularization of activity patterns in noisy neural networks. Together, these findings suggest that intelligence may ride on computation noise to promote near-optimal decision-making in adverse conditions without any engineered cognitive sophistication.

neuroscience↗

bioRxiv2019

Imprecise neural computations as source of human adaptive behavior in volatile environments

Everyday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforcement learning. Thus, minimal inferential capabilities may have evolved along with imprecise neural computations as contributing to near-optimal adaptive behavior in real-life environments, while leading humans to make suboptimal choices in canonical decision-making tasks.

neuroscience↗

bioRxiv2018

Believing in one's power: a counterfactual heuristic for goal-directed control

Most people envision themselves as operant agents endowed with the capacity to bring about changes in the outside world. This ability to monitor one's own causal power has long been suggested to rest upon a specific model of causal inference, i.e., a model of how our actions causally relate to their consequences. What this model is and how it may explain departures from optimal inference, e.g., illusory control and self-attribution biases, are still conjecture. To address this question, we designed a series of novel experiments requiring participants to continuously monitor their causal influence over the task environment by discriminating changes that were caused by their own actions from changes that were not. Comparing different models of choice, we found that participants' behaviour was best explained by a model deriving the consequences of the forgone action from the current action that was taken and assuming relative divergence between both. Importantly, this model agrees with the intuitive way of construing causal power as \"difference-making\" in which causally efficacious actions are actions that make a difference to the world. We suggest that our model outperformed all competitors because it closely mirrors people's belief in their causal power - a belief that is well-suited to learning action-outcome associations in controllable environments. We speculate that this belief may be part of the reason why reflecting upon one's own causal power fundamentally differs from reasoning about external causes.

neuroscience↗

bioRxiv2018

Computational noise in reward-guided learning drives behavioral variability in volatile environments

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions which fail to maximize expected value. We reasoned that these non-greedy decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here, using reinforcement learning (RL) models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stems from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by BOLD responses to obtained rewards in the dorsal anterior cingulate cortex (dACC) and by phasic pupillary dilation - suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine (LC-NE) system. Together, these findings indicate that most of behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

neuroscience↗